Abstract

This research investigates the development and implementation of an AI-driven digital health assistant for chronic disease management, addressing challenges in delivering continuous, personalized care for conditions like diabetes, hypertension, cardiovascular disease, and chronic respiratory disorders. Using a mixed-methods approach—systematic literature review, stakeholder interviews, and prototype development—the study identifies barriers to AI adoption, including data interoperability, clinician acceptance, patient engagement, and regulatory compliance. The proposed framework integrates machine learning, natural language processing, and predictive analytics to provide personalized recommendations, medication adherence monitoring, symptom tracking, and clinical decision support. Stakeholder analysis involved healthcare providers, patients, developers, and policymakers, with prototype testing conducted across diverse patient groups. Findings highlight the potential of AI-driven assistants to improve outcomes, reduce costs, and enhance quality of life, with innovations like adaptive algorithms, EHR integration, and multilingual support. Critical success factors include robust data governance, provider training, and sustainable financing. The study underscores opportunities for preventive care, early intervention, and population health management, while emphasizing ethical considerations, privacy, and equitable access.